Fitting partially linear functional-coefficient panel-data models with Stata

Du, Kerui; Zhang, Yonghui; Zhou, Qiankun

doi:10.1177/1536867x20976339

Cited by 31 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, based on the research of (An et al, 2016) and with reference to (Du et al, 2020c) to estimate the partial linear panel model of the function coefficients, the Stata software package estimates the following models. The specific estimation equation in this paper is set as follows with reference to the reference model Eq.…”

Section: Research Modelmentioning

confidence: 99%

Environmental regulation, agricultural green technology innovation, and agricultural green total factor productivity

Sun

2022

Front. Environ. Sci.

View full text Add to dashboard Cite

In order to promote the green transformation of agricultural development, we used a partial linear function coefficient panel model to measure the impact of environmental regulations in 30 provinces and cities in China on agricultural green technology innovation and agricultural green total factor productivity. The advantage of this model is that it can take into account the heterogeneity of regional economic development levels, that is, by introducing variables that are functions of regional economic development levels as coefficients of environmental regulation. The research results show that: when the level of regional economic development is low, environmental regulation has a limited impact on agricultural green technology innovation and agricultural green total factor productivity, but as the level of regional economic development gradually increases, environmental regulation has a more significant impact on the two. And environmental regulation has a greater impact on agricultural green total factor productivity than on agricultural green technology innovation. Based on the research results, policy recommendations are suggested.

show abstract

Section: Research Modelmentioning

confidence: 99%

Environmental regulation, agricultural green technology innovation, and agricultural green total factor productivity

Sun

2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…The PLFC model is flexible and capable of accommodating the nonlinear structure and the heterogeneity across cross‐sections and time (Du et al, 2020). The PLFC model allows for linearity in some regressors and nonlinearity in others (Du et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…To account for nonlinearity in this nexus, we fit a partially linear functional‐coefficient (PLFC) model proposed by Yonghong et al (2016). The PLFC model is semiparametric in nature and accounts for heterogeneity over individuals and times as well as allows for linearity in some regressors and nonlinearity in other regressors (Du et al, 2020). In this study, we consider the effect of economic complexity on environmental degradation nexus as a nonparametric function of income.…”

Section: Methodsmentioning

confidence: 99%

“…The B‐splines sieve method is used for the approximation of the unknown function,

g ((), {italicINC}_{italicit})

. The performance of the sieve method depends on the number of approximation terms, which is determined by the number of knots (Du et al, 2020). The optimal number of knots required by the sieve method is determined by the least‐squares cross‐validation method.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Economic complexity and environmental degradation: Evidence from OECD countries

Aluko

Acheampong

2022

Bus Strat Env

View full text Add to dashboard Cite

We augment the existing knowledge on the role of economic complexity in the environment and sustainable development debate by examining the effect of economic complexity on environmental degradation (measured by ecological footprint, CO 2 emissions, N 2 O emissions and greenhouse gas emissions) contingent on income, using data from 35 OECD countries between 1998 and 2017. With the fixed effects model estimator, we find that income facilitates economic complexity to mitigate ecological footprint, CO 2 emissions, N 2 O emissions and greenhouse gas emissions. Also, we fit a partial linear functional-coefficient model to find that income influences economic complexity to exert a nonlinear effect on ecological footprint, CO 2 emissions, N 2 O emissions and greenhouse gas emissions. We find that economic complexity leads to an increase in ecological footprint, CO 2 emissions, N 2 O emissions and greenhouse gas emissions at lower income levels but gradually dampens them as income rises. Finally, by applying the Method of Moments Quantile regression to control for distributional heterogeneity, we also find that the mitigating effect of economic complexity on ecological footprint, CO 2 emissions, N 2 O emissions and greenhouse gas emissions is transmitted through income across quantiles. The policy implications are discussed.

show abstract

“…3Detailed techniques on the functional-coefficient model can be found in Du et al (2020) and Zhang and Zhou (2021).…”

mentioning

confidence: 99%

Escape from air pollution: How does air quality in the place of residence shape tourism consumption?

Wang

Fang

et al. 2022

Tourism Economics

Self Cite

View full text Add to dashboard Cite

Air quality has been demonstrated to be an important determinant influencing tourist decision making. In this paper, we investigate the effect of air quality in the place of origin on a resident’s tourism consumption and the moderating effect of household income on this relationship. We develop a conceptual framework to rationalize the effect and empirically examine it at the household level using China Labor-force Dynamics Survey data from 2016. The results show that poor ambient air quality increases tourism consumption. Furthermore, this relationship becomes stronger for the higher-income group: One standard deviation increase in the Air Quality Index would increase tourism expenditure as much as a 22.56% increase in family income. The methods of instrumental variable and functional-coefficient regression were employed for robustness analysis. These findings contribute to the literature by providing a new perspective for tourism demand studies and direct implications for tourism management and policy making.

show abstract

Fitting partially linear functional-coefficient panel-data models with Stata

Cited by 31 publications

References 18 publications

Environmental regulation, agricultural green technology innovation, and agricultural green total factor productivity

Environmental regulation, agricultural green technology innovation, and agricultural green total factor productivity

Economic complexity and environmental degradation: Evidence from OECD countries

Escape from air pollution: How does air quality in the place of residence shape tourism consumption?

Contact Info

Product

Resources

About